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It’s the infrastructure and the expertise in training models that have been to purpose of the investments. These companies know full well that the models themse
by audunw 2y ago
It’s the infrastructure and the expertise in training models that have been to purpose of the investments. These companies know full well that the models themselves are nearly worthless in the long term. They’ve said so explicitly that the models are not a moat. All they can do is make sure they have the compute and the engineers to continue to stay at or near the state of the art, while building up a customer base and integrations that add value on top of the model itself.
It doesn’t help if you have a cheap model if you don’t have the infrastructure to run it at a large scale, and the integrations that help pull in regular mass market consumers.
The other companies will just copy, and possibly surpass the breakthrough in efficiency. And now they’ve got an efficient model AND the infrastructure and expertise to deploy it at a huge scale very rapidly.
This month it’s Deepseek that’s ahead. Next month it will be someone else. Haven’t we learned that by now?
- maxglute 2y ago>models themselves are nearly worthless It makes all the difference when they also know 90% of their capex is worthless. Obviously hyperbole, but grossly over valued for what was originally scaled. And with compute infra depreciating 3-5 years, it doesn't matter whose ahead next month, if what they're actually ahead in is massive massive debt due to loss making infra outlays that will never return on capita because their leading model now can only recoop a fraction of that after open source competitors drove prices down for majority of good enough use cases. The lesson one should learn is economics 101 still applies. If you borrow billions on a moat, and 100s of billions on a wall, but competitors invent a canon, then you're still potentially very dead, just also very indebt while doing so.